1. Field of the Invention
The present invention generally relates to statistical modeling and analysis. More particularly, the invention relates to development of predictive models.
2. Background Art
Statistical modeling and analysis replaced the use of rule-based decision making during the last decade. Predictive modeling is a form of statistical analysis that is increasingly being used in customer management, underwriting, assessment of business patterns, customer loyalty, product portfolio performances, pricing variations, and so forth. Predictive modeling involves development of mathematical constructs that enable reliable prediction of future events or measurements based on historical information. The results may further be exploited for decision-making, which is related to the profitability of an organization.
Prediction of future events or measurements of a problem under investigation is performed by analyzing modeling variables. The modeling variables are related to different attributes and characteristics of the problem. The number of modeling variables utilized for predictive modeling has grown exponentially over the past few years. In some cases, the number of modeling variables may be up to 10,000 or even more. This leads to increased time and resource requirements for predictive modeling.
Further, it is essential to identify the relationship between a dependent variable and the modeling variables. The manual development of predictive models makes the identification difficult and leads to inclusion of redundant modeling variables. The inclusion of redundant modeling variables may lead to incorrect parameter estimation, increased computation time, confounding interpretations, and increased time requirement for building a predictive model. The manual development may also require more time.
Given the foregoing, what is needed is a method to reduce time requirements for predictive modeling. Further, the method should develop predictive models without manual intervention. The method should also enable manual modification and verification of the developed predictive models.